{"title":"用于过敏性休克算法的人工智能和机器学习。","authors":"Christopher Miller, Michelle Manious, Jay Portnoy","doi":"10.1097/ACI.0000000000001015","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose of review: </strong>Anaphylaxis is a severe, potentially life-threatening allergic reaction that requires rapid identification and intervention. Current management includes early recognition, prompt administration of epinephrine, and immediate medical attention. However, challenges remain in accurate diagnosis, timely treatment, and personalized care. This article reviews the integration of artificial intelligence and machine learning in enhancing anaphylaxis management.</p><p><strong>Recent findings: </strong>Artificial intelligence and machine learning can analyze vast datasets to identify patterns and predict anaphylactic episodes, improve diagnostic accuracy through image and biomarker analysis, and personalize treatment plans. Artificial intelligence-powered wearable devices and decision support systems can facilitate real-time monitoring and early intervention. The ethical considerations of artificial intelligence use, including data privacy, transparency, and bias mitigation, are also discussed.</p><p><strong>Summary: </strong>Future directions include the development of predictive models, enhanced diagnostic tools, and artificial intelligence-driven educational resources. By leveraging artificial intelligence and machine learning, healthcare providers can improve the management of anaphylaxis, ensuring better patient outcomes and advancing personalized medicine.</p>","PeriodicalId":10956,"journal":{"name":"Current Opinion in Allergy and Clinical Immunology","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence and machine learning for anaphylaxis algorithms.\",\"authors\":\"Christopher Miller, Michelle Manious, Jay Portnoy\",\"doi\":\"10.1097/ACI.0000000000001015\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose of review: </strong>Anaphylaxis is a severe, potentially life-threatening allergic reaction that requires rapid identification and intervention. Current management includes early recognition, prompt administration of epinephrine, and immediate medical attention. However, challenges remain in accurate diagnosis, timely treatment, and personalized care. This article reviews the integration of artificial intelligence and machine learning in enhancing anaphylaxis management.</p><p><strong>Recent findings: </strong>Artificial intelligence and machine learning can analyze vast datasets to identify patterns and predict anaphylactic episodes, improve diagnostic accuracy through image and biomarker analysis, and personalize treatment plans. Artificial intelligence-powered wearable devices and decision support systems can facilitate real-time monitoring and early intervention. The ethical considerations of artificial intelligence use, including data privacy, transparency, and bias mitigation, are also discussed.</p><p><strong>Summary: </strong>Future directions include the development of predictive models, enhanced diagnostic tools, and artificial intelligence-driven educational resources. By leveraging artificial intelligence and machine learning, healthcare providers can improve the management of anaphylaxis, ensuring better patient outcomes and advancing personalized medicine.</p>\",\"PeriodicalId\":10956,\"journal\":{\"name\":\"Current Opinion in Allergy and Clinical Immunology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Opinion in Allergy and Clinical Immunology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/ACI.0000000000001015\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/7/24 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"ALLERGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Allergy and Clinical Immunology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/ACI.0000000000001015","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/24 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ALLERGY","Score":null,"Total":0}
Artificial intelligence and machine learning for anaphylaxis algorithms.
Purpose of review: Anaphylaxis is a severe, potentially life-threatening allergic reaction that requires rapid identification and intervention. Current management includes early recognition, prompt administration of epinephrine, and immediate medical attention. However, challenges remain in accurate diagnosis, timely treatment, and personalized care. This article reviews the integration of artificial intelligence and machine learning in enhancing anaphylaxis management.
Recent findings: Artificial intelligence and machine learning can analyze vast datasets to identify patterns and predict anaphylactic episodes, improve diagnostic accuracy through image and biomarker analysis, and personalize treatment plans. Artificial intelligence-powered wearable devices and decision support systems can facilitate real-time monitoring and early intervention. The ethical considerations of artificial intelligence use, including data privacy, transparency, and bias mitigation, are also discussed.
Summary: Future directions include the development of predictive models, enhanced diagnostic tools, and artificial intelligence-driven educational resources. By leveraging artificial intelligence and machine learning, healthcare providers can improve the management of anaphylaxis, ensuring better patient outcomes and advancing personalized medicine.
期刊介绍:
This reader-friendly, bimonthly resource provides a powerful, broad-based perspective on the most important advances from throughout the world literature. Featuring renowned guest editors and focusing exclusively on one to three topics, every issue of Current Opinion in Allergy and Clinical Immunology delivers unvarnished, expert assessments of developments from the previous year. Insightful editorials and on-the-mark invited reviews cover key subjects such as upper airway disease; mechanisms of allergy and adult asthma; paediatric asthma and development of atopy; food and drug allergies; and immunotherapy.